Introducción¶
En esta prĆ”ctica vamos a entrenar varios clasificadores sobre un mismo dataset, usando todos los algoritmos de clasificación que hemos visto hasta ahora e implementĆ”ndolos usando las librerĆas adecuadas.
Dataset¶
El dataset que usaremos es "Breast Cancer Wisconsin (Diagnostic)", disponible en UCI Machine Learning Repository
TambiƩn estƔ disponible directamente en sklearn.datasets.load_breast_cancer().
Descripción del dataset
| Aspecto | Detalle |
|---|---|
| Tipo de problema | Clasificación binaria |
| Objetivo | Diagnosticar si un tumor es maligno (1) o benigno (0) |
| NĆŗmero de muestras | 569 |
| NĆŗmero de variables | 30 caracterĆsticas numĆ©ricas |
| Balance de clases | Moderadamente balanceado (~37% malignos, ~63% benignos) |
Métrica¶
Elige la mƩtrica adecuada para evaluar tus modelos. Justifica tu respuesta.
El dataset no estĆ” perfectamente balanceada, por lo que accuracy puede inducir sesgos (un modelo que siempre predice ābenignoā tendrĆa ~63% de acierto).
El AUC-ROC mide la capacidad discriminativa del modelo (quƩ tan bien separa positivos y negativos) independientemente del umbral, y es mƔs informativa cuando las clases son desiguales.
Es ademĆ”s aplicable y comparable entre modelos probabilĆsticos (NaĆÆve Bayes, Regresión LogĆstica, Redes Bayesianas) y no probabilĆsticos (CART).
1. Cargar el dataset e importar librerĆas¶
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, KBinsDiscretizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import roc_auc_score, roc_curve, auc, confusion_matrix
# Cargar dataset
data = datasets.load_breast_cancer()
print(data.keys())
# Separar X e y
X = data["data"]
y = data["target"]
feature_names = data.feature_names
target_names = data.target_names
dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])
Información detallada sobre el dataset:
print(data.DESCR)
.. _breast_cancer_dataset:
Breast cancer Wisconsin (diagnostic) dataset
--------------------------------------------
**Data Set Characteristics:**
:Number of Instances: 569
:Number of Attributes: 30 numeric, predictive attributes and the class
:Attribute Information:
- radius (mean of distances from center to points on the perimeter)
- texture (standard deviation of gray-scale values)
- perimeter
- area
- smoothness (local variation in radius lengths)
- compactness (perimeter^2 / area - 1.0)
- concavity (severity of concave portions of the contour)
- concave points (number of concave portions of the contour)
- symmetry
- fractal dimension ("coastline approximation" - 1)
The mean, standard error, and "worst" or largest (mean of the three
worst/largest values) of these features were computed for each image,
resulting in 30 features. For instance, field 0 is Mean Radius, field
10 is Radius SE, field 20 is Worst Radius.
- class:
- WDBC-Malignant
- WDBC-Benign
:Summary Statistics:
===================================== ====== ======
Min Max
===================================== ====== ======
radius (mean): 6.981 28.11
texture (mean): 9.71 39.28
perimeter (mean): 43.79 188.5
area (mean): 143.5 2501.0
smoothness (mean): 0.053 0.163
compactness (mean): 0.019 0.345
concavity (mean): 0.0 0.427
concave points (mean): 0.0 0.201
symmetry (mean): 0.106 0.304
fractal dimension (mean): 0.05 0.097
radius (standard error): 0.112 2.873
texture (standard error): 0.36 4.885
perimeter (standard error): 0.757 21.98
area (standard error): 6.802 542.2
smoothness (standard error): 0.002 0.031
compactness (standard error): 0.002 0.135
concavity (standard error): 0.0 0.396
concave points (standard error): 0.0 0.053
symmetry (standard error): 0.008 0.079
fractal dimension (standard error): 0.001 0.03
radius (worst): 7.93 36.04
texture (worst): 12.02 49.54
perimeter (worst): 50.41 251.2
area (worst): 185.2 4254.0
smoothness (worst): 0.071 0.223
compactness (worst): 0.027 1.058
concavity (worst): 0.0 1.252
concave points (worst): 0.0 0.291
symmetry (worst): 0.156 0.664
fractal dimension (worst): 0.055 0.208
===================================== ====== ======
:Missing Attribute Values: None
:Class Distribution: 212 - Malignant, 357 - Benign
:Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian
:Donor: Nick Street
:Date: November, 1995
This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.
https://goo.gl/U2Uwz2
Features are computed from a digitized image of a fine needle
aspirate (FNA) of a breast mass. They describe
characteristics of the cell nuclei present in the image.
Separating plane described above was obtained using
Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree
Construction Via Linear Programming." Proceedings of the 4th
Midwest Artificial Intelligence and Cognitive Science Society,
pp. 97-101, 1992], a classification method which uses linear
programming to construct a decision tree. Relevant features
were selected using an exhaustive search in the space of 1-4
features and 1-3 separating planes.
The actual linear program used to obtain the separating plane
in the 3-dimensional space is that described in:
[K. P. Bennett and O. L. Mangasarian: "Robust Linear
Programming Discrimination of Two Linearly Inseparable Sets",
Optimization Methods and Software 1, 1992, 23-34].
This database is also available through the UW CS ftp server:
ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/
.. dropdown:: References
- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction
for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on
Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
San Jose, CA, 1993.
- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and
prognosis via linear programming. Operations Research, 43(4), pages 570-577,
July-August 1995.
- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)
163-171.
Veamos cómo son las clases:
print(data.target_names)
print(np.unique(data.target))
['malignant' 'benign'] [0 1]
Por lo tanto, este dataset no sigue la convención usual y tenemos que:
0 -> 'malignant'
1 -> 'benign'
# Cargar como dataframe, con y en la columna llamada 'target'
df = pd.DataFrame(data=data["data"], columns=data["feature_names"])
df["target"] = y
print("Clase (counts):")
print(df['target'].value_counts())
Clase (counts): target 1 357 0 212 Name: count, dtype: int64
2. AnÔlisis exploratorio¶
df.columns
Index(['mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness', 'mean compactness', 'mean concavity',
'mean concave points', 'mean symmetry', 'mean fractal dimension',
'radius error', 'texture error', 'perimeter error', 'area error',
'smoothness error', 'compactness error', 'concavity error',
'concave points error', 'symmetry error', 'fractal dimension error',
'worst radius', 'worst texture', 'worst perimeter', 'worst area',
'worst smoothness', 'worst compactness', 'worst concavity',
'worst concave points', 'worst symmetry', 'worst fractal dimension',
'target'],
dtype='object')
# Histograma por clase
sns.countplot(data=df, x="target")
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting. INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
<Axes: xlabel='target', ylabel='count'>
# Histogramas de algunas variables por clase
columns_histogram = ["mean radius", "mean symmetry", "radius error", "symmetry error", "worst radius", "worst symmetry"]
for c in columns_histogram:
sns.histplot(data=df,
x=df[c],
hue=df["target"])
plt.show()
# Todos los pairplots posibles, segmentados por clase.
# Usa Seaborn pairplot
sns.pairplot(data=df, hue="target")
<seaborn.axisgrid.PairGrid at 0x19fc7918a90>